Detection of thin lines for selective sensitivity during reticle inspection using processed images

ABSTRACT

A detection method for a spot image based thin line detection is disclosed. The method includes a step for constructing a band limited spot image from a transmitted and reflected optical image of the mask. The spot image is calibrated to reduce noise introduced by the one or more inspection systems. Based on the band limited spot image, a non-printable feature map is generated for the non-printable features and a printable feature map is generated for the printable features. One or more test images of the mask are analyzed to detect defects on such mask. A sensitivity level of defect detection is reduced in areas of the one or more test images defined by the non-printable feature map, as compared with areas of the one or more test images that are not defined by the non-printable features map

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit under 35 U.S.C. §120 to U.S.application Ser. No. 13/473,299, filed May 16, 2012, titled “DETECTIONOF THIN LINES FOR SELECTIVE SENSITIVITY DURING RETICLE INSPECTION USINGPROCESSED IMAGES”, by Zhengyu Wang et al., which claims the benefit ofthe following prior applications: (i) U.S. Provisional Application No.61/608,268, filed Mar. 8, 2012, titled “IMPROVED DETECTION OF THIN LINEFOR SELECTIVE SENSITIVITY DURING RETICLE INSPECTION USING PROCESSEDIMAGES” by Zhengyu Wang et al., (ii) U.S. Provisional Application No.61/609,359, filed Mar. 11, 2012, titled “DETECTION OF THIN LINES FORSELECTIVE SENSITIVITY DURING RETICLE INSPECTION USING PROCESSED IMAGES”by Zhengyu Wang et al., and (iii) U.S. Provisional Application No.61/609,903, filed Mar. 12, 2012, titled “DETECTION OF THIN LINES FORSELECTIVE SENSITIVITY DURING RETICLE INSPECTION USING PROCESSED IMAGES”by Zhengyu Wang et al. These applications are herein incorporated byreference in their entirety for all purposes.

TECHNICAL FIELD OF THE INVENTION

The invention generally relates to a field of reticle inspection. Moreparticularly the present invention relates to a method to detect thinlines using a reticle inspection tool.

BACKGROUND

As densities and complexities of integrated circuits (ICs) continue toincrease, inspecting photolithographic mask patterns becomeprogressively more challenging. Every new generation of ICs has denserand more complex patterns that currently reach and exceed opticallimitations of lithographic systems. To overcome these opticallimitations, various Resolution Enhancement Techniques (RET), such asOptical Proximity Correction (OPC), have been introduced. For example,OPC helps to overcome some diffraction limitations by modifyingphotomask patterns such that the resulting printed patterns correspondto the original desired patterns. Such modifications can includeperturbations to sizes and edges of main IC features, i.e., printablefeatures. Other modifications involve additions of serifs to patterncorners and/or providing nearby sub-resolution assist features (SRAFs),which are not expected to result in printed features and, therefore, arereferred to as non-printable features. These non-printable features areexpected to cancel pattern perturbations that would otherwise haveoccurred during the printing process. However, OPC makes mask patternseven more complex and usually very dissimilar to resulting wafer images.Furthermore, OPC defects often do not translate into printable defects.

Non-printable and printable features have different effects on resultingprinted patterns and often need to be inspected using differentinspection parameters, e.g., sensitivity levels. Areas containingnon-printable features are typically “de-sensed” to avoid falsepositives during inspection. Conventional inspection methods generallyrely on user defined characteristics, such as feature sizes, fordifferentiating between printable and non-printable features.

SUMMARY

The following presents a simplified summary of the disclosure in orderto provide a basic understanding of certain embodiments of theinvention. This summary is not an extensive overview of the disclosureand it does not identify key/critical elements of the invention ordelineate the scope of the invention. Its sole purpose is to presentsome concepts disclosed herein in a simplified form as a prelude to themore detailed description that is presented later.

In general, certain embodiments of the invention use processed maskimages, instead of the original optical images, for thin line (ornon-printable feature) detection. The use of processed mask imagesresults in improved segmentation and main feature protection. Certainembodiments of the invention include: construction of a band limitedspot image from transmitted and reflected optical images that are usedfor thin line detection purpose; an option to calibrate and compensateoptical aberrations from the spot image; a way to restore a mask patternfrom the spot image, to allow more reliable segmentation and moreaccurate line width measurement; and a way to distinguish thin line andlarger geometries and prevent thin line growth from encroaching largegeometries.

In one embodiment, a method for inspecting a photolithographic mask toidentify lithographically significant defects is provided. A maskcomprising a plurality of printable features and a plurality ofnon-printable features is provided. The mask is configured to achievelithographic transfer of the printable features onto a substrate using alithography system. A transmitted image and a reflected image of themask is produced by one or more inspection systems. A band limited spotimage is constructing based on the transmitted and reflected images toreduce noise introduced by the inspection apparatus. The spot image isrestored to a mask image to thereby minimize further optical aberrationsfrom the one or more inspection systems in the mask image. The restoredmask image is used to generate a non-printable feature map for thenon-printable features and printable feature map for the printablefeatures. The non-printable feature map is expanded (or grown) whilepreventing encroachment of such non-printable feature map into theprintable features based on the printable feature map. One or more testimages of the mask may then be analyzed to detect defects on such mask,wherein a sensitivity level of defect detection is reduced in areas ofthe one or more test images defined by the non-printable feature map, ascompared with areas of the one or more test images that are not definedby the non-printable features map.

In specific implementations, a thinning process is performed on therestored mask image to generate a skeleton image, and line widths aremeasured in the restored mask image using the skeleton image todetermine where to measure such line widths. The measured line widthsare used to generate the non-printable and printable feature maps bydistinguishing between measured line widths that are below or equal toor above a specified threshold value, respectively. In anotherembodiment, generation of the band limited spot image is accomplished bycombining the reflected and transmitted images into a linear equationwith selected coefficients so that high frequency terms cancel eachother out. In a further aspect, the spot image is processed usingcalibration data to remove further optical aberrations from the spotimage. In another aspect, each feature that has a measured line widthbelow the predefined threshold value is only included in thenon-printable map if such feature is within a predefined distance toanother feature that has a measured line width equal or above thepredefined threshold value. In yet another embodiment, restoring thespot image to the mask image results in the mask image being deblurredand a truer image of the mask.

In certain embodiments, the invention pertains to a system forinspecting a photomask to identify lithographically significant defectsthat includes at least one memory and at least one processor that areconfigured to perform at least some of the above described operations.In other embodiments, the invention pertains to computer readable mediahaving instructions stored thereon for performing at least some of theabove described operations.

In another embodiment, detection method for thin line detection on amask is disclosed. The method comprises (i) generating a band limitedspot image from a transmitted and reflected optical image of the mask;(ii) calibrating the spot image to minimize a plurality of opticalaberrations from the spot image; (iii) restoring the spot image back toa mask image to allow at least one of: a more reliable segmentationbetween thin line and non-thin line areas on the mask image or a moreaccurate line width measurement for facilitating segmentation; (iv)distinguishing between thin line features and non-thin lines features onthe restored mask image; and (v) growing areas containing thin linefeatures while preventing the thin line growth from encroaching thenon-thin line features.

In a specific embodiment, preventing the thin line growth fromencroaching the plurality of geometries is accomplished bydistinguishing the thin line and non-thin features. In another aspect,the detection method further includes a step of utilizing level-setfunction to measure critical dimensions for performing thin linedetection. In another features, the step of restoring the mask patternfrom the spot image allows more reliable segmentation and more accurateline width measurement. In yet another embodiment, the spot image is aband limited low passed version of the mask pattern.

These and other aspects of the invention are described further belowwith reference to the figures.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A illustrates a base pattern provided on a photomask in accordancewith certain embodiments.

FIG. 1B illustrates a resulting wafer image of the base pattern in FIG.1A after a lithographic transfer.

FIG. 2 is a flowchart of a procedure for thin line detection for aphotomask based on a band limited spot image according to an exemplaryembodiment of the present invention.

FIG. 3 illustrates obtaining varying line widths at different locationson a circle contour.

FIG. 4 is a screenshot of an inspection report according to an exemplaryembodiment of the present invention.

FIG. 5 is a flowchart illustrating a reticle inspection procedure inaccordance with one embodiment of the present invention.

FIG. 6A is a simplified schematic representation of a lithographicsystem for transferring a mask pattern from a photomask onto a wafer inaccordance with certain embodiments.

FIG. 6B provides a schematic representation of a photomask inspectionapparatus in accordance with certain embodiments.

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS

In the following description, numerous specific details are set forth inorder to provide a thorough understanding of the present invention. Thepresent invention may be practiced without some or all of these specificdetails. In other instances, well known process operations have not beendescribed in detail to not unnecessarily obscure the present invention.While the invention will be described in conjunction with the specificembodiments, it will be understood that it is not intended to limit theinvention to the embodiments.

Introduction

One photomask inspection method uses an approach for separatingprintable features (also referred to as main features) fromnon-printable ones (also referred to as thin lines) for later“de-sensing” areas containing non-printable features. A test imageand/or reference image are used to create a feature map based on a ruleset by a user. Usually, a user defines a line width as criteria forseparating non-printable features from printable ones.

One approach may start with capturing reference and test images. Anexample technique is described in U.S. Pat. No. 8,090,189 to VinayakPhalke et al., which is incorporated herein by reference in its entiretyfor all purposes. In general, an intensity threshold is applied to theseimages to define a foreground of the features, which is a collection ofthe image areas with intensity values below this threshold.

Photomasks are often designed with different types of thin lines, suchas opaque thin lines and clear thin lines, which add additionalcomplexities in defining a foreground. Opaque thin lines are thinstripes or dots of molybdenum-silicon that appear darker than theirsurroundings on a transmitted image. On the hand, clear thin lines arethin stripes or dots of cuts made on molybdenum-silicon/chrome surfacesthat appear darker than these surrounding surfaces on a reflected image.Furthermore, in a die-to-die inspection, defect areas are not apparentfrom the test and reference images. As such, a combined analysis of thereference and test images needs to be performed. In a next operation,line widths of foreground features are measured to differentiate betweenthin lines and main features to form a “raw” feature map. A rule forthis line width threshold is typically set by a user.

When generating an optical image, distortions in the image may result inovershoot or undershoot of particular features, e.g., due to blurring orother optical distortions. For example, OMOG (Opaque MoSi on Glass)reflected images may result in high undershoot. Such effects often tendto complicate the segmentation process. For example, overshoots mayserve as distractions in the segmentation process and have to beanalyzed more carefully to determine how to segment the particularovershoot feature into a thin line or non-thin line. In sum, overshootsand undershoots can add complexity to the segmentation process.

The line width definition tends to be subjective and depends on a singlethreshold (contour level) which is arbitrarily chosen by a user. Theline width as a result has no direct correlation with the underlyingmask pattern's true dimension. It often results in the user having toreiterate multiple times to find the threshold to properly segment thinlines. Additionally, a user often needs to set the contour level forboth the clear thin line and the opaque thin line separately whichdoubles the amount of set up work. Finally, when a thin line's linewidth is small, or when a thin line has other patterns in its closeneighborhood, the modulation of the thin lines can vary a lot. Sometimesit is simply impossible to find a single contour level to segment allthin lines of the same tone.

Additionally, this prior approach does not specify large geometryprotection due to the nature of thin line detection. For example, adefective main feature (printable feature) can appear in theneighborhood of a detected thin line, and a dilation margin is usuallyapplied to expand the original detected thin line to cover someneighborhood area, which may then encompass the defective main feature.Without main feature protection, such dilation has the risk of runninginto main features, as well as associated defects, and as a resultde-sense lithographically critical defects on large printable geometriesin the neighborhood of a non-printable thin line.

Some of the above issues can be further illustrated in the followingexample. FIG. 1A illustrates an illustrative base pattern provided on aphotomask, while FIG. 1B illustrates a resulting wafer image of thatbase pattern. There is very little, if any, resemblance between the twoimages. Extensive uses of OPC lead to such discrepancies.

Inspecting Methods Examples

FIG. 2 is a flowchart of a procedure 200 for thin line detection for aphotomask based on a band limited spot image according to an exemplaryembodiment of the present invention. In the examples described herein,thin line detection includes detection of any non-printable oflithographically insignificant feature of the photomask. The terms “thinline”, “non-printable”, and “lithographically insignificant” are usedherein interchangeably.

In general, any suitable type of photomask (reticle) may be used in theprocess. For example, a photomask made from a transparent fused silicablank with a pattern defined by a chrome metal adsorbing film can beused. In general, a photomasks or mask may take the form of any suitabletype of reticle or photomask, e.g., phase shift masks, and EmbeddedPhase Shift Masks (EPSMs). A photomask generally includes a plurality ofprintable features and a plurality of non-printable features.

A printable feature can be defined as a feature that appears on aresulting wafer image. Such printed feature may or may not be present onthe resulting wafer in the same shape or form as on a photomask. Forexample, FIG. 1A illustrates a base pattern provided on a photomask,while FIG. 1B illustrates a resulting wafer image of that base pattern.Therefore, in the context of a photomask, a printable feature may beunderstood as an area corresponding to the printable feature on a waferplane. Non-printable features (or “thin lines”) may include variousoptical proximity correction (OPC) features that are used to compensatefor imaging errors due to diffraction and other reasons. One type ofsuch non-printable features is sub-resolution assist features (SRAF).

Once the photomask is provided for the inspection process, e.g., placedon an inspection stage of the inspection system, a reflected image and atransmitted image of the photomask are provided in operation 202. Moregenerally, the photomask may be illuminated to capture two or more lightintensity images at different illumination and/or collection conditions.In illustrated embodiment, a transmitted light intensity image and areflected light intensity image are captured. In other embodiments, twoor more other types of images may be used.

The captured test images are typically aligned in operation 204. Thisalignment may involve matching optical properties of the inspectionsystem(s) for multiple test and reference images. For example, in thecase of transmitted and reflected images, some adjustment of the imagescan be made to compensate for differences in optical paths of the tworespective signals. Alignment adjustments may depend on specificgeometries of an inspection system used. In the illustrated embodiment,alignment involves aligning the transmitted image with respect to thereflected image.

Once aligned, a spot image may be constructed based on the reflected andtransmitted images in operation 206. The spot image is also referred toas a band limited mask image. The process for constructing a spot imagemay generally include substantially eliminating optical noise from thetransmitted and reflected images to obtain a resulting spot image. Ingeneral, high frequency effects are substantially reduced or eliminated.For example, rings that are formed around particular reticle patternsdue to optical effects of the inspection system are removed in the spotimage. The spot image results in a reduction in the amount of overshootand undershoot, which could otherwise distract thin lines detection.That is, noise in the mask image is substantially reduced, and suchnoise can no longer be detected as thin lines. The reflected andtransmitted images can be combined with selected coefficients in alinear combination so that the high frequencies terms cancel each otherout. As a result, the spot image is a band limited low passed version ofthe mask pattern image.

In one approach, partially coherent optical imaging can be modeled as asum of two or more coherent systems, which is further explained in moredetail in U.S. Pat. 7,873,204 by Wihl et al, which is incorporatedherein by reference for purposes of describing operation 206. In thisexample implementation, the Hopkins equations for partially coherentimaging can be used to form a Transmission-Cross-Coefficient (TCC)matrix. This matrix can be then decomposed into corresponding Eigenvectors, which act as kernels of coherent systems. The Eigen valueweighted sum of the intensity contributions from each of these coherentsystems yields the image intensity, which can be used to represent theintensity of the transmitted signal. In certain embodiments, reflectedand transmitted intensities of the test images can be represented withonly linear terms that are referred to as band limited mask amplitudefunctions. An example of this function is presented in Equation 1.

$\begin{matrix}{\frac{{{a_{R}}^{2}\left( {{I_{T}\left( {x,y} \right)} - {c_{T}}^{2}} \right)} - {{a_{T}}^{2}\left( {{I_{R}\left( {x,y} \right)} - {c_{R}}^{2}} \right.}}{{2{a_{R}}^{2}{{Re}\left( {a_{T}C_{T}^{*}} \right)}} - {2{a_{T}}^{2}{{Re}\left( {a_{R}c_{R}^{*}} \right)}}} = {{\sum{\lambda_{i}{D_{i}\left\lbrack {{P\left( {x,y} \right)} \oplus {E_{i}\left( {x,y} \right)}} \right\rbrack}}} = {{{P\left( {x,y} \right)} \oplus {\sum\limits_{i = 0}^{N}{\lambda_{i}D_{i}{E_{i}\left( {x,y} \right)}}}} = {M\left( {x,y} \right)}}}} & \left\lbrack {{Equation}\mspace{14mu} 1} \right\rbrack\end{matrix}$

where α_(R) is the complex reflected amplitude of the difference betweenthe mask foreground tone and the background tone; I_(T) (x,y) describesthe transmitted intensity image of a mask using the inspection system;C_(T) is the complex transmitted amplitude of the background tone of themask (e.g., in a quartz and chrome binary mask C_(T) can describeproperties of the chromium pattern); α_(T) is the complex transmittedamplitude of the difference between the mask foreground tone and thebackground tone (e.g., using the same mask as above α_(T) can describethe optical properties of the difference between the quartz and thechromium; C_(T) and α_(T) of course vary depending on the properties ofthe material layers described); I_(R) (x,y) describes the reflectedintensity image of a mask using the inspection system; C_(R) is thecomplex reflected amplitude of the background tone of the mask and α_(R)is the complex reflected amplitude of the difference between the maskforeground tone and the background tone; Re (x) represents the realcomponent of x; P(x,y) defines the mask pattern of the photomask beinginspected; E_(i) and λ_(i) refer, respectively, to the Eigen Vectors andEigen Values of associated elements of a transmission cross-coefficient(TCC) imaging matrix associated with the inspection tool; D_(i) is theDC gain of E_(i).

The band limited mask pattern M (x,y) is defined by the mask pattern P(x,y) convolved with a function: Σ_(i=0) ^(N)λ_(i)D_(i)E_(i)(x, y),which is referred to as a “recovery kernel”. Therefore, the band limitedmask pattern is a modified version of the mask pattern function P (x,y).

Although combining the reflected and transmitted images results in somehigh frequency portions of the image, which are due to optical effects,to be cancelled, the spot image construction is still an approximationof the “true” mask (e.g., without aberrations) because some aberrationsremain in the spot image. In a further embodiment, the constructed spotimage can be processed to compensate for certain aberrations. Forexample, a method can be used to calibrate static aberrations offlineand then filter aberration effects from the spot image. In general, avariety of patterns may be fabricated on a calibration mask and thenimaged. The aberration terms in the calibration image can then beextracted for the different patterns. The extracted aberration terms canthen be subtracted from images of similar patterns of the spot image sothat such aberration terms are removed from the spot image.

After the spot image is constructed, a restoration inversion process maybe performed to obtain a restored mask image in operation 208. That is,a more “true” or binary-like mask image is derived from the spot imagein an inversion process. For example, patterns in the spot image thatmay be blurred by the optics are sharpened to form a mask image. Forvery thin line or thin line that is not well separated from otherpatterns in the neighborhood, this post-processing will facilitateeasier and less ambiguous segmentation.

For a die-to-die inspection process, a simple high pass with automaticthreshold may be used so as to create sharper transitions in the spotimage. In certain inspection tools, such as the StarLight™ availablefrom KLA-Tencor Corp. of Milpitas, Calif., a level-set based inversionis provided in the tool to deduce the underlying mask pattern. Anexample level set function is described in U.S. Pat. Application Pub.No. 2010/0251203 by Daniel Abrams, filed 30 Sep. 2010, which applicationis incorporated herein by reference for describing a level-set process.A level set function can be used to define the boundaries (or contours)on the mask, which function is incrementally and iteratively adjusteduntil the restored mask image is found. For each set of mask contours,the optical inspection tool, which was used to obtain the original maskimage, is modeled or simulated on the set of mask contours to result ina simulated spot image. The mask contour set is adjusted and a simulatedspot image based on each adjusted set of contours is generated until asimulated spot image most closely matches the constructed spot image.For example, a minimum difference is achieved. The set of mask contoursthat results in a simulated spot image that most closely matches theconstructed spot image can then be defined as the restored mask image.

The level set function may take any suitable form for defining theboundary of each image mask feature. In one embodiment, the level setfunction is equal to 0 at the boundary or contour; less than 0 foroutside the contour; and more than 0 for inside the contour. The levelset 0 function (or whatever function defines the contours) can be usedto measure critical dimension (CD) on the mask image. That is, CD can bedirectly measured on the 0^(th) level set function (or the definedcontours). For example, four CD measurements on each feature contour maybe taken in four directions and the minimum is defined as the CD forsuch feature.

The resulting mask image includes patterns, geometries, features,shapes, etc. (“patterns”), and the patterns are typically defined by aset of contours, lines, boundaries, edges, curves, etc. (“contours”),which generally surround, enclose, and/or define the boundary of thevarious regions which constitute a pattern on the mask. Line width maybe difficult to ascertain on particular contours. For example, the linewidth measurement of a circle contour may vary depending on where themeasurement is obtained as illustrated in FIG. 3. As shown, a line width302 measured through the center of the circle will be much larger than aline width 304 measured through an edge of the circle.

Accordingly, a process may be used to determine where to measure linewidths on the image mask. In one embodiment, a thinning process may beperformed on the mask image to obtain a skeleton image in operation 210.In general, each pattern of the mask image is reduced in size so as toprovide a site or location in the mask image for later measuring linewidth on the particular mask image pattern. For instance, the skeletonfor a circle is a pixel in the center of the circle so that the linewidth is obtained through the center of the circle on the mask image,and the skeleton for a line is a line that is 1 pixel wide so that theline width of the line can be obtained anywhere along the line'slongitudinal axis.

After the mask image is thinned to produce a skeleton image, theskeleton and mask images are then used to determine whether line widthsin the mask image can be defined as thin line patterns or non-thin limepatters in operation 212. This line width check results in both a thinline map and a non-thin line map. In general, the skeleton image is usedto measure line widths on the mask image and compare the measured linewidths to a line width specification for thin lines (or non-thin lines).If the measured line width is less than the line width specification,the associated pattern is defined as a thin line. The line width checkmay also include only defining a feature as a thin-line if such featureis within a predefined proximity to a non-thin feature. If the measuredline width is equal to or greater than the line width specification, theassociated pattern is defined as a non-thin line. Thus, particularpatterns on the mask image can be defined as thin line or non-thin lineareas to produce both a thin line and non-thin line map.

For die-to-die, the binary mask image can first be normalized and then atemplate pixel algorithm can be applied. In general, this process mayinclude comparing the rate of change of pixel intensity in theneighborhood of a test pixel, which is defined by the skeleton image,against the theoretical drop off for a thin line (e.g., SRAF) of a givensize or line width.

When a level set function is provided, the zero crossing of the functionis the particular contour itself A direct measurement of line width onthis contour along multiple directions can be performed. Finally, takingthe minimum of these measurements can then be used to approximate theline width. For main feature protection, the line width check can be thesole criteria to decide if a pattern is thin line or large line pattern.

Further when pixels have the same tone as the thin line, but their linewidths are larger than the user defined line width specification, suchpixels can be treated as non-thin line pixels. These non-thin linepixels form the large geometry map (or non-thin line map) image thatneeds to be protected against thin line growth. During thin line growth,this image can be used as the mask to stop unwanted growth in operation214. Thus, thin line growth is prevented from encroaching largegeometries areas of the mask image to result in a final thin-line map(or inversely non-thin line map), referred to herein as a feature map.

FIG. 4 is a screenshot of an inspection report according to an exemplaryembodiment of the present invention compared to an older process. Thereflected optical image 302 shows a large amount of undershoot. The thinline map is shown as “feature map” (306 for old method and 312 for thespot-image method). A dark background in each feature map (306 and 312)indicts that there is no thin line found. The dark gray lines in the“feature map” 306 from the old method are thin line candidate pixels,but were rejected later for other reasons. They are only shown forillustration purposes. The bright white lines in the “feature map” 312from the new method are thin line pixels that passed further checksincluding line width check and closeness to main features, and thereforeare accepted as true thin lines. The light gray areas surrounding thewhite lines are thin line growth to cover the neighborhood to a userspecified extent.

As can be seen in FIG. 4, the old method failed to segment the two thinlines due to them closely butting against other patterns. Instead theold method was distracted by some candidate thin line pixels on the edgeof contacts due to undershoot despite such pixels being disqualifiedlater for other reasons. The new method instead, not only caught thethin lines properly, but avoided distraction from the false thin linecandidates. The resulting feature map is not only more accurate, butalso more robust and computationally more efficient with lessdistraction.

The different approaches used in die-to-die and selection of a set levelfunction option are considered alternative aspects. Advantages ofcertain embodiments of the present invention may include more reliablethin line segmentation because it largely avoids overshoot or undershootpresent in the optical images. Certain embodiments may also largelyovercomes the limitation of die-to-die thin line detection where it maybe very difficult or impossible to segment thin lines of differentmodulation and those close to neighbor patterns. Certain embodiments mayalso be more user friendly because it automatically selects contour,instead of making user arbitrarily set contour. Further, a singlecontour can be used for both clear and dark thin line, instead of twocontours. Thin line measurement may also be better correlated withunderlying thin line width on the mask, and thin line growth intode-sense critical defects on large geometries may be avoided.

FIG. 5 is a flowchart illustrating a reticle inspection procedure 500 inaccordance with one embodiment of the present invention. Initially,different inspection thresholds may be associated with different reticleareas based on the final feature map in operation 502. Photomaskinspection methods may involve providing one or more user-defineddetection thresholds. For example, areas defined as main features by thefeature map may be assigned one detection threshold, while areascontaining SRAFs or other non-printable thin line feature may beassigned a lower threshold. This differentiation can be used to optimizeinspection resources.

A reference image may be provided in operation 504. For example, anotherimage of a die area on the reticle is obtained for a die-to-die typeinspection. In a die-to-database inspection, a reference image isgenerated based on the design database. For example, the inspectionoptics are modeled and applied to the design patterns to obtain areference image. The reference image may be aligned with respect to thetest image in operation 506. Both the test and reference images may bemask recovered spot images as described herein or “raw” images obtainedfrom the inspection tool.

In operation 508, the reference image is compared to test image based oninformation contained in the feature map. For example, test andreference images may be divided into multiple areas identified in thefeature map. Each set of areas containing a test image area and acorresponding reference image area may be inspected individually. MEEFs,user defined thresholds, geometrical map, and other information specificfor each area may be used in this operation. In other words, analysis ofthe test image may involve identifying portions of the test image andcorresponding portions of the reference image and identifying anydifferences in these images for each identified portion. In a specificembodiment, differences are identified between aligned test transmittedand reference transmitted images and between aligned test reflected andthe reference reflected images.

It may then be determined based on the comparison results whether thereticle passes inspection in operation 510. If the reticle passes, theinspection process may end, and fabrication may proceed using thepassing reticle. If the reticle does not pass, the reticle can either berepaired or discarded in operation 512 and inspection ends.

In general, the feature map can be specifically used to define and focuson areas that contain lithographically significant features and defectsduring reticle inspection. The map can be used to provide instructionsto the inspection system to “de-sense” areas defined as thin-line ornonprintable features during inspection. For example, areas containingonly thin lines (e.g., SRAFs) may be inspected with lower sensitivity,than areas containing main features (printable or non-thin linefeatures). As indicated above, areas of the thin line feature mapdistinguish between these two types of features. Overall, novelprocesses and inspection systems described herein allow a more effectivereticle inspection process.

In yet another embodiment, the method may also include constructing ageometrical map based on the band limited mask pattern for classifyinggeometrical features into one or more geometrical feature types, such asedges, corners, and line ends. Furthermore, a process of identifying thelithographically significant defects can be enhanced by applyingdifferent detection thresholds to different geometrical feature types ofthe geometrical map.

In certain embodiments, inspection is applied to multiple tone masks aswell. One example of such masks are tri-tone masks having a darkestregion (e.g., a chrome or opaque regions) and a quartz or lightestregion with a pattern of grey scale regions having a darkness betweenthe two. Such grey scale regions can be obtained in a number of ways(e.g., using EPSM materials and so on). In this case, the mask istreated as two different masks that are separately analyzed. Forexample, a tri-tone mask can be treated using the same techniques asdescribed above. However, the tri-tone mask can be treated as a maskhaving a background pattern (e.g., chromium) with the grey scale pattern(e.g., EPSM material) treated as the foreground. The images can beprocessed as above using the same equations and process operations. Asecond analysis is performed on the mask using the EPSM material as thebackground pattern and the lightest pattern (e.g., the quartz) treatedas the foreground. Alignment can easily be effectuated because each ofthe materials have substantially differing properties that demonstratedifferent edge effects which can be used to align the images. The maskpatterns can then be summed and then compared to references indie-to-die or die-to-database comparisons to verify wafer patterncorrectness throughout the process window and to identifylithographically significant defects.

SYSTEM EXAMPLES

FIG. 6A is a simplified schematic representation of a typicallithographic system 600 that can be used to transfer a mask pattern froma photomask M onto a wafer W in accordance with certain embodiments.Examples of such systems include scanners and steppers, morespecifically PAS 5500 system available from ASML in Veldhoven,Netherlands. In general, an illumination source 603 directs a light beamthrough an illumination lens 605 onto a photomask M located in a maskplane 602. The illumination lens 605 has a numeric aperture 601 at thatplane 602. The value of the numerical aperture 601 impacts which defectson the photomask are lithographic significant defects and which ones arenot. A portion of the beam that passes through the photomask M forms apatterned optical signal that is directed through imaging optics 653 andonto a wafer W to initiate the pattern transfer.

FIG. 6B provides a schematic representation of an inspection system 650that has an imaging lens 651 a with a relative large numerical aperture651 b at a reticle plane 652 in accordance with certain embodiments. Thedepicted inspection system 650 includes microscopic magnification optics653 designed to provide, for example, 60-200× magnification for enhancedinspection. The numerical aperture 65 lb at the reticle plane 652 of theinspection system is often considerable greater than the numericalaperture 601 at the reticle plane 602 of the lithography system 600,which would result in differences between test inspection images andactual printed images. Each of these optical systems (600, 650) inducesdifferent optical effects in the produced images, which are accountedand compensated for in novel inspection techniques described herein.

The inspection techniques described herein may be implemented on variousspecially configured inspection systems, such as the one schematicallyillustrated in FIG. 6B. The system 650 includes an illumination source660 producing a light beam that is directed through illumination optics651 onto a photomask M in the reticle plane 652. Examples of lightsources include lasers or filtered lamps. In one example, the source isa 193 nm laser. As explained above, the inspection system 650 has anumerical aperture 651 b at the reticle plane 652 that may be greaterthan a reticle plane numerical aperture (e.g., element 601 in FIG. 6A)of the corresponding lithography system. The photomask M to be inspectedis placed at the reticle plane 652 and exposed to the source.

The patterned image from the mask M is directed through a collection ofmagnification optical elements 653, which project the patterned imageonto a sensor 654. Suitable sensors include charged coupled devices(CCD), CCD arrays, time delay integration (TDI) sensors, TDI sensorarrays, photomultiplier tubes (PMT), and other sensors. In a reflectingsystem, optical elements would direction and capture the reflectedimage.

The signals captured by the sensor 654 can be processed by a computersystem 673 or, more generally, by a signal processing device, which mayinclude an analog-to-digital converter configured to convert analogsignals from the sensor 654 into digital signals for processing. Thecomputer system 673 may be configured to analyze intensity, phase,and/or other characteristics of the sensed light beam. The computersystem 673 may be configured (e.g., with programming instructions) toprovide a user interface (e.g., on a computer screen) for displayingresultant test images and other inspection characteristics. The computersystem 673 may also include one or more input devices (e.g., a keyboard,mouse, joystick) for providing user input, such as changing detectionthreshold. In certain embodiments, the computer system 673 is configuredto carry out inspection techniques detailed below. The computer system673 typically has one or more processors coupled to input/output ports,and one or more memories via appropriate buses or other communicationmechanisms.

Because such information and program instructions may be implemented ona specially configured computer system, such a system includes programinstructions/computer code for performing various operations describedherein that can be stored on a computer readable media. Examples ofmachine-readable media include, but are not limited to, magnetic mediasuch as hard disks, floppy disks, and magnetic tape; optical media suchas CD-ROM disks; magneto-optical media such as optical disks; andhardware devices that are specially configured to store and performprogram instructions, such as read-only memory devices (ROM) and randomaccess memory (RAM). Examples of program instructions include bothmachine code, such as produced by a compiler, and files containinghigher level code that may be executed by the computer using aninterpreter.

In certain embodiments, a system for inspecting a photomask includes atleast one memory and at least one processor that are configured toperform the following operations: producing test light intensity imagesof a mask that include a test transmitted image and a test reflectedimage, constructing a spot image, restoring the spot image to a maskimage, line thinning, creating a feature map, and analyzing the testlight intensity images using the feature map to identify photomaskdefects. One example of an inspection system includes a speciallyconfigured TeraScan™ DUV inspection system available from KLA-Tencor ofMilpitas, Calif.

Although the foregoing invention has been described in some detail forpurposes of clarity of understanding, it will be apparent that certainchanges and modifications may be practiced within the scope of theappended claims. It should be noted that there are many alternative waysof implementing the processes, systems, and apparatus of the presentinvention. Accordingly, the present embodiments are to be considered asillustrative and not restrictive, and the invention is not to be limitedto the details given herein.

What is claimed is:
 1. A method for inspecting a photolithographic maskto identify lithographically significant defects, the method comprising:providing a mask comprising a plurality of printable features and aplurality of non-printable features, the mask configured to achievelithographic transfer of the printable features onto a substrate using alithography system; by one or more inspection systems, producing atransmitted image and a reflected image of the mask; constructing a bandlimited spot image based on the transmitted and reflected images toreduce noise introduced by the one or more inspection systems; restoringthe spot image to a restored mask image so that results in the restoredmask image are deblurred and a truer image of the mask is obtained;based on the restored mask image, generating a non-printable feature mapfor the non-printable features and printable feature map for theprintable features; and analyzing one or more test images of the mask todetect defects on such mask, wherein a sensitivity level of defectdetection is reduced in areas of the one or more test images defined bythe non-printable feature map, as compared with areas of the one or moretest images that are not defined by the non-printable features map. 2.The method of claim 1, further comprising: performing a thinning processon the restored mask image to generate a skeleton image; and measuringline widths in the restored mask image using the skeleton image todetermine where to measure such line widths, wherein the measured linewidths are used to generate the non-printable and printable feature mapsby distinguishing between measured line widths that are below or equalto or above a specified threshold value, respectively.
 3. The method ofclaim 1, wherein constructing the band limited spot image isaccomplished by combining the reflected and transmitted images into alinear equation with selected coefficients so that high frequency termscancel each other out.
 4. The method of claim 2, further comprising:processing the spot image using calibration data to remove furtheroptical aberrations from the spot image.
 5. The method of claim 2,wherein each feature that has a measured line width below the predefinedthreshold value is only included in the non-printable map if suchfeature is within a predefined distance to another feature that has ameasured line width equal or above the predefined threshold value. 6.The method of claim 1, wherein the analysis of the one or more testimages is accomplished by comparing the one or more test images to areference image obtained from a design database for fabricating themask.
 7. The method of claim 1, wherein the analysis of the one or moretest images is accomplished by comparing the one or more test images toa reference image obtained from a reference die.
 8. The method of claim1, wherein the non-printable features comprise Sub-Resolution AssistFeatures (SRAF).
 9. The method of claim 1, further comprisingconstructing a geometrical map based on the spot image and forclassifying geometrical features into one or more geometrical featuretypes selected from the group consisting of edges, corners, and lineends, wherein the analysis of the one or more test images is furtherbased on the geometrical map.
 10. An inspection system for inspecting amask to identify lithographically significant defects comprising atleast one memory and at least one processor that are configured toperform the following operations: providing a mask comprising aplurality of printable features and a plurality of non-printablefeatures, the mask configured to achieve lithographic transfer of theprintable features onto a substrate using a lithography system;producing a transmitted image and a reflected image of the mask;constructing a band limited spot image based on the transmitted andreflected images to reduce noise introduced by the inspection system;restoring the spot image to a restored mask image to so that results inthe restored mask image are deblurred and a truer image of the mask isobtained; based on the restored mask image, generating a non-printablefeature map for the non-printable features and printable feature map forthe printable features; and growing the non-printable feature map whilepreventing encroachment of such non-printable feature map into theprintable features based on the printable feature map; and analyzing oneor more test images of the mask to detect defects on such mask, whereina sensitivity level of defect detection is reduced in areas of the oneor more test images defined by the non-printable feature map, ascompared with areas of the one or more test images that are not definedby the non-printable features map.
 11. The system of claim 10, furthercomprising: performing a thinning process on the restored mask image togenerate a skeleton image; and measuring line widths in the restoredmask image using the skeleton image to determine where to measure suchline widths, wherein the measured line widths are used to generate thenon-printable and printable feature maps by distinguishing betweenmeasured line widths that are below or equal to or above a specifiedthreshold value, respectively.
 12. The system of claim 10, whereinconstructing the band limited spot image is accomplished by combiningthe reflected and transmitted images into a linear equation withselected coefficients so that high frequency terms cancel each otherout.
 13. The system of claim 11, further comprising: processing the spotimage using calibration data to remove further optical aberrations fromthe spot image.
 14. The system of claim 11, wherein each feature thathas a measured line width below the predefined threshold value is onlyincluded in the non-printable map if such feature is within a predefineddistance to another feature that has a measured line width equal orabove the predefined threshold value.
 15. The system of claim 10,wherein the analysis of the one or more test images is accomplished bycomparing the one or more test images to a reference image obtained froma design database for fabricating the mask.
 16. The system of claim 10,wherein the analysis of the one or more test images is accomplished bycomparing the one or more test images to a reference image obtained froma reference die.
 17. The system of claim 10, wherein the non-printablefeatures comprise Sub-Resolution Assist Features (SRAF).
 18. The systemof claim 10, wherein the at least one memory and the at least oneprocessor are further configured for constructing a geometrical mapbased on the spot image and for classifying geometrical features intoone or more geometrical feature types selected from the group consistingof edges, corners, and line ends, wherein the analysis of the one ormore test images is further based on the geometrical map.